Open Access Journal

ISSN : 2394 - 6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

ISSN : 2394-6849 (Online)

Simulation and Analysis Of Advanced Transform Based Image Segmentation

Author : Amrutha.R.S 1 Dr.K.M.Sadyojatha 2

Date of Publication :7th May 2016

Abstract: An important task in image processing is to segment the given image into more meaningful regions and to label the individual regions. Texture segmentation is used to mark out the boundary between different textures according to texture cues. Human is sensitive to three texture properties: repetition, directionality and complexity.Image segmentation can be classified into three categories: A) Supervised. – These methods require the interactivity in which the pixels belonging to the same intensity range pointed out manually and segmented. B) Automatic. – This is also known as unsupervised methods, where the algorithms need some priori information, so these methods are more complex, and C) Semi-automatic that is the combination of manual and automatic segmentation.

Reference :

  1. 1) Schmid P. “Segmentation of Digitized Dermatoscopic Images by Two-Dimensional Color Clustering,” IEEE Trans. On Medical Imaging, 1999. vol. 18 no.2 pp.164-171.

    2) Cucchiara R, Grana C, Seidenari S, Pellacani G. “Exploiting Color and Topological Features for Region Segmentation with Recursive Fuzzy c-means” Machine Graphics and Vision 2002; vol. 11 no.2/3 pp.169-182.

    3) Erkol B, Moss RH, Stanley RJ et al., “Automatic Lesion Boundary Detection in Dermoscopy Images Using Gradient Vector Flow Snakes” Skin Research and Technology 2005; vol. 11 no.1 pp.17- 26.

    4) Nikhil R Pal and Sankar K Pal “A review on image segmentation techniques”. Pattern Recognition, 1993; vol.26 no.9 pp.1277-1294

    5) Melli R, Grana C, Cucchiara R. “Comparison of Color Clustering Algorithms for Segmentation of Dermatological Images,” Proc. Of the SPIE Medical Imaging 2006 Conf., 6144: 1211-1219

    6) Comaniciu, D. & Meer, P. (2002). Mean shift: “A robust approach toward feature space analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24 no.5 pp. 603–619.


  2. 1) Schmid P. “Segmentation of Digitized Dermatoscopic Images by Two-Dimensional Color Clustering,” IEEE Trans. On Medical Imaging, 1999. vol. 18 no.2 pp.164-171.

    2) Cucchiara R, Grana C, Seidenari S, Pellacani G. “Exploiting Color and Topological Features for Region Segmentation with Recursive Fuzzy c-means” Machine Graphics and Vision 2002; vol. 11 no.2/3 pp.169-182.

    3) Erkol B, Moss RH, Stanley RJ et al., “Automatic Lesion Boundary Detection in Dermoscopy Images Using Gradient Vector Flow Snakes” Skin Research and Technology 2005; vol. 11 no.1 pp.17- 26.

    4) Nikhil R Pal and Sankar K Pal “A review on image segmentation techniques”. Pattern Recognition, 1993; vol.26 no.9 pp.1277-1294

    5) Melli R, Grana C, Cucchiara R. “Comparison of Color Clustering Algorithms for Segmentation of Dermatological Images,” Proc. Of the SPIE Medical Imaging 2006 Conf., 6144: 1211-1219

    6) Comaniciu, D. & Meer, P. (2002). Mean shift: “A robust approach toward feature space analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24 no.5 pp. 603–619.


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